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Founded in 2019, Rock Bund Capital is a proprietary trading firm deeply committed to shaping the future of the cryptocurrency industry. We have an average daily trading volume reaching $1 billion and peak daily trading volume of $9 billion USD. We process over 15 million transactions daily, trading more than 1,000 symbols across major CEx and DEx.
Our team combines expertise in traditional finance, quantitative research, and advanced engineering with a deep understanding of blockchain technology. This unique blend enables us to excel in trading across complex crypto markets, including both CeFi and DeFi, while providing capital and strategic guidance to projects that drive innovation and foster sustainable growth in the crypto industry.
We are looking for a Quantitative Researcher to develop and enhance mid-frequency (MFT) intraday systematic trading strategies across crypto markets (CEX spot/perp/futures) or traditional financial markets (commodities, equities, index futures) .
This role is research-intensive , with a strong focus on alpha discovery, signal generation, and portfolio construction , working closely with trading, infrastructure, and engineering teams to bring ideas from prototype into production.
Alpha Discovery: Conduct end-to-end alpha research and design intraday predictive strategies (e.g., cross-asset statistical arbitrage) across Crypto (spot/perps) and TradFi (commodities, equities, index futures).
Modeling & Backtesting: Build statistical/ML models for return and volatility prediction; develop rigorous backtesting frameworks with realistic transaction cost and market impact simulation.
Execution & Optimization: Refine intraday portfolio construction (turnover-aware, risk budgeting) and optimize execution logic (scheduling, passive/aggressive trade-offs).
Production & Lifecycle: Partner with engineers for strategy deployment, continuously monitor live PnL, track signal decay, and iterate on risk controls.
Degree: BS / MS / PhD in a highly quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Engineering, Operations Research, etc.).
Mathematical Foundation: Strong grasp of probability, stochastic processes, time-series analysis, and intraday econometrics.
Technical Stack: Proficient in Python for rapid prototyping. Comfortable with Linux, Git, and large-scale data pipelines (e.g., Parquet/Arrow, ClickHouse). C++ is a strong plus.
Domain Expertise: Proven track record in quant research or systematic trading, with experience handling large intraday datasets (bars, ticks, order book snapshots).
Asset Class Familiarity: Exposure to at least one core market: Crypto CEX, Commodity Futures, or Global Equities/Index Futures.
Advanced Modeling: Application of Machine Learning (e.g., gradient boosting, sequential models) for short-horizon or intraday alpha generation.
Microstructure & Execution: Deep understanding of order book dynamics, slippage modeling, and execution benchmarking (Implementation Shortfall, arrival price).
Trading Track Record: A demonstrable record of profitable live strategies (Sharpe, capacity, and turnover metrics can be discussed under NDA).
Elite Problem Solving: Background in Competitive Programming (ACM-ICPC), Math Olympiads, Kaggle, or published research in top-tier quant finance/ML journals.
Competitive remuneration package and a m eritocratic culture where accomplishments are rewarded
Fast paced and result-oriented with a flat structure
Teams collaborate in a casual working environment
Excellent exposure to the digital asset ecosystem and the latest market insight
Great career development opportunities
Disclaimer We do not accept unsolicited resumes from any professional staffing or search firms. All resumes, or any other information identifying potential candidates, shared with any employee of Rock Bund Capital or its affiliates by any method without a standing signed supplier agreement will be deemed free to contact without restrictions, and no placement fee of any kind will be paid in the event the candidate is hired by Rock Bund Capital or any of its affiliates.
Qualifications: Onsite, Quantitative Researcher, develop and enhance mid-frequency (MFT) intraday systematic trading strategies, across crypto markets (CEX spot/perp/futures) or traditional financial markets (commodities, equities, index futures), research-intensive, strong focus on alpha discovery, signal generation, and portfolio construction, working closely with trading, infrastructure, and engineering teams, bring ideas from prototype into production, Conduct end-to-end alpha research, design intraday predictive strategies (e.g., cross-asset statistical arbitrage), Build statistical/ML models for return and volatility prediction, develop rigorous backtesting frameworks with realistic transaction cost and market impact simulation, Refine intraday portfolio construction (turnover-aware, risk budgeting), optimize execution logic (scheduling, passive/aggressive trade-offs), Partner with engineers for strategy deployment, continuously monitor live PnL, track signal decay, and iterate on risk controls, BS / MS / PhD in a highly quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Engineering, Operations Research, etc.), Strong grasp of probability, stochastic processes, time-series analysis, and intraday econometrics, Proficient in Python for rapid prototyping, Comfortable with Linux, Git, and large-scale data pipelines (e.g., Parquet/Arrow, ClickHouse), C++ is a strong plus, Proven track record in quant research or systematic trading, experience handling large intraday datasets (bars, ticks, order book snapshots), Exposure to at least one core market: Crypto CEX, Commodity Futures, or Global Equities/Index Futures, Application of Machine Learning (e.g., gradient boosting, sequential models) for short-horizon or intraday alpha generation (Preferred), Deep understanding of order book dynamics, slippage modeling, and execution benchmarking (Implementation Shortfall, arrival price) (Preferred), A demonstrable record of profitable live strategies (Sharpe, capacity, and turnover metrics can be discussed under NDA) (Preferred), Background in Competitive Programming (ACM-ICPC), Math Olympiads, Kaggle, or published research in top-tier quant finance/ML journals (Preferred)
Rockbund
Rockbund